This paper proposes a road-slope estimation algorithm to improve the performance and efficiency of intelligent vehicles. The algorithm integrates three types of road-slope measurements from a GPS receiver, automotive onboard sensors, and a longitudinal vehicle model. The measurement integration is achieved through a probabilistic data association filter (PDAF) that combines multiple measurements into a single measurement update by assigning statistical probability to each measurement and by removing faulty measurement via the false-alarm function of the PDAF. In addition to the PDAF, an interacting multiple-model filter (IMMF) approach is applied to the slope estimation algorithm to allow adaptation to various slope conditions. The model set of the IMMF is composed of a constant-slope road model (CSRM) and a constant-rate slope road model (CRSRM). The CSRM assumes that the slope of the road is always constant, and the CRSRM assumes that the slope of the road changes at a constant rate. The IMMF adapts the road-slope model to the driving conditions. The developed algorithm is verified and evaluated through experimental and case studies using a real-time embedded system. The results show that the performance and efficiency of the road-slope estimation algorithm is accurate and reliable enough for intelligent vehicle applications.


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    Titel :

    Real-Time Road-Slope Estimation Based on Integration of Onboard Sensors With GPS Using an IMMPDA Filter


    Beteiligte:
    Jo, Kichun (Autor:in) / Kim, Junsoo (Autor:in) / Sunwoo, Myoungho (Autor:in)


    Erscheinungsdatum :

    01.12.2013


    Format / Umfang :

    3646435 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch